My research aims to harness the full potential of machine learning models. By leveraging the accuracy and efficiency of these models, we can achieve precise predictions of high-order derivatives without the need for explicit training on them. This capability extends to calculating Hessians on potential energy surfaces, which are essential for optimizing transition states, as well as predicting chemical shifts with ab initio quality and force field efficiency.